Learn to Think in Color

Why Do We Love Statistics That Aren’t Relevant? We are drawn to statistics because patterns make an uncertain world feel predictable, even when those patterns have little connection to what happens next. An impressive percentage gives a claim authority. A long streak suggests consistency, while several decimal places create the impression of careful measurement. Yet none of these qualities establishes whether the information is relevant to a decision. The important distinction is between describing what happened and explaining why it happened. Historical statistics become useful when the relationships behind them help us understand present conditions. When those relationships are missing or no longer apply, even perfectly accurate numbers can mislead. Why Numbers Make Patterns Convincing People naturally search for relationships in their surroundings. Recognizing meaningful patterns helps us understand and respond to events, but the same tendency can make coincidence look like evidence...

Does Correlation Always Mean Causation? The Dangerous Illusion of Connection in a Data-Driven World When two things appear to move together, it can be tempting to assume one causes the other. But in the world of data, jumping to conclusions can lead us astray. Understanding the difference between  correlation  and  causation  is vital for clear thinking, whether you’re analyzing business trends, health data, or societal shifts. In this post, we unravel these commonly confused concepts, explore hidden variables, and look at how scientists and statisticians determine true causal links. What Is Correlation? Correlation  refers to a statistical relationship between two variables. When one changes, the other tends to change in a predictable way. This can be positive (both increase or decrease together) or negative (one increases while the other decreases). Think of it like two people riding an escalator—they go up together, but not because one is pulling the other. T...